Data scientists in our team bring scientific and statistical methods to bear on the issues of advertising product creation, development and improvement with a deep, data-driven appreciation for the behaviors of the end user and the ecosystem. As a Data Scientist working on Ads Insights and Measurement, you will develop, evaluate and improve the entire range of Google's advertising products including Search, Display, Apps, TV and Video (YouTube). You will collaborate closely with a multi-disciplinary team of engineers, analysts and product managers to develop new science and to translate it into deployed products at scale. You will also play a key role in developing new ideas and methods that drive ad measurement and facilitate agreements, including paradigm-shifting ad-measurement science and products for the privacy-preserving future of digital advertising. In doing so, you will be a key part of building and driving impact on large-scale ad-systems both at Google and in the ad-tech and mar-tech industry as a whole, globally.
In this role, you will be quantitatively trained with expertise in quantitative methodologies, and with a solid understanding of statistics and causal inference methods. You will have an interest in understanding consumer behavior, advertising and privacy with a passion for business problems, and an interest in combining data and strategy will grow in our environment. You will be able to leverage their training and their skills to leverage data and technology to make business decisions. You will be comfortable working cross-functionally and grow in a changing, science-driven organization.
Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
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benefits at Google.
Responsibilities
- Help suggest, support, and shape new data-driven advertising measurement products, with a primary focus on lift and incrementality measurement.
- Design and analyze large-scale experiments—including both user-level and geo-based randomized controlled trials—to evaluate advertising effectiveness and establish causal impact.
- Develop and implement advanced quantitative methods by combining causal inference, statistical modeling, and machine learning to solve complex analysis problems related to conversion lift.
- Conduct end-to-end analyses of large, complex data sets, building scalable and iterative analysis pipelines to provide incrementality insights to our partners at scale.
- Work cross-functionally to provide business recommendations, translating complex experimental findings into clear, actionable insights for engineering, product leaders, and key stakeholders.
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
Preferred qualifications:
- 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
- Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
- Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
- Applied experience with machine learning on large datasets.
- Understand potential outcomes framework and have familiarity with causal inference methods such as split-testing, instrumental variables, fixed effects regression, panel data models, regression discontinuity, matching estimators.